Stroke causes systemic immunosuppression. T lymphocytes are involved in infarct size in the early stages of stroke. However, the phenotypes of T lymphocytes and their functions in peripheral immune organs and the brain have not been well analyzed in the acute and chronic phases of stroke. Here, we investigated pathological phenotypic alterations in the systemic immune response, especially changes in T lymphocytes, from one day to six months after ischemic stroke in mice. Impairment in thymocyte numbers, development, proliferation, and apoptosis were observed for up to two weeks. The number of mature T cells in the spleen and blood decreased and showed reduced interferon-γ production. Increased numbers of CD4-CD8-CD3+ double-negative T cells were observed in the mouse brain during the early stages of stroke, whereas interleukin (IL)-10+Foxp3+ regulatory T lymphocytes increased from two weeks during the chronic phase. These phenotypes correlated with body weight and neurological severity scores. The recovery of T lymphocyte numbers and increases in IL-10+Foxp3+ regulatory T lymphocytes may be important for long-term neurological outcomes. Dynamic changes in T lymphocytes between the acute and chronic phases may play different roles in pathogenesis and recovery. This study provides fundamental information regarding the T lymphocyte alterations from the brain to the peripheral immune organs following stroke.

Stroke causes systemic immunosuppression. T lymphocytes are involved in infarct size in the early stages of stroke. However, the phenotypes of T lymphocytes and their functions in peripheral immune organs and the brain have not been well analyzed in the acute and chronic phases of stroke. Here, we investigated pathological phenotypic alterations in the systemic immune response, especially changes in T lymphocytes, from one day to six months after ischemic stroke in mice. Impairment in thymocyte numbers, development, proliferation, and apoptosis were observed for up to two weeks. The number of mature T cells in the spleen and blood decreased and showed reduced interferon-γ production. Increased numbers of CD4-CD8-CD3+ double-negative T cells were observed in the mouse brain during the early stages of stroke, whereas interleukin (IL)-10+Foxp3+ regulatory T lymphocytes increased from two weeks during the chronic phase. These phenotypes correlated with body weight and neurological severity scores. The recovery of T lymphocyte numbers and increases in IL-10+Foxp3+ regulatory T lymphocytes may be important for long-term neurological outcomes. Dynamic changes in T lymphocytes between the acute and chronic phases may play different roles in pathogenesis and recovery. This study provides fundamental information regarding the T lymphocyte alterations from the brain to the peripheral immune organs following stroke.

Purpose: By utilizing both protein and mRNA expression patterns, we can identify more detailed and diverse immune cells, providing insights into understanding the complex immune landscape in cancer ecosystems. Materials and Methods: This study was performed by obtaining publicly available Cellular Indexing of Transcriptomes and Epitopes by Sequencing (CITE-seq) data of peripheral blood mononuclear cells (PBMCs) from the Gene Expression Omnibus database. A total of 94674 total cells were analyzed, of which 32412 were T cells. There were 228 protein features and 16262 mRNA features in the data.The Seurat package was used for quality control and preprocessing, principal component analysis was performed, and Uniform Manifold Approximation and Projection was used to visualize the clusters. Protein and mRNA levels in the CITE-seq were analyzed. Results: We observed that a subset of T cells in the clusters generated at the protein level divided better. By identifying mRNA markers that were highly correlated with the CD4 and CD8 proteins and cross-validating CD26 and CD99 markers using flow cytometry, we found that CD4 + and CD8+ T cells were better discriminated in PBMCs. Weighted Nearest Neighbor clustering results identified a previously unobserved T cell subset. Conclusion In this study, we used CITE-seq data to confirm that protein expression patterns could be used to identify cells more precisely. These findings will improve our understanding of the heterogeneity of immune cells in the future and provide valuable insights into the complexity of the immune response in health and disease.

Purpose: By utilizing both protein and mRNA expression patterns, we can identify more detailed and diverse immune cells, providing insights into understanding the complex immune landscape in cancer ecosystems. Materials and Methods: This study was performed by obtaining publicly available Cellular Indexing of Transcriptomes and Epitopes by Sequencing (CITE-seq) data of peripheral blood mononuclear cells (PBMCs) from the Gene Expression Omnibus database. A total of 94674 total cells were analyzed, of which 32412 were T cells. There were 228 protein features and 16262 mRNA features in the data.The Seurat package was used for quality control and preprocessing, principal component analysis was performed, and Uniform Manifold Approximation and Projection was used to visualize the clusters. Protein and mRNA levels in the CITE-seq were analyzed. Results: We observed that a subset of T cells in the clusters generated at the protein level divided better. By identifying mRNA markers that were highly correlated with the CD4 and CD8 proteins and cross-validating CD26 and CD99 markers using flow cytometry, we found that CD4 + and CD8+ T cells were better discriminated in PBMCs. Weighted Nearest Neighbor clustering results identified a previously unobserved T cell subset. Conclusion In this study, we used CITE-seq data to confirm that protein expression patterns could be used to identify cells more precisely. These findings will improve our understanding of the heterogeneity of immune cells in the future and provide valuable insights into the complexity of the immune response in health and disease.

Purpose: By utilizing both protein and mRNA expression patterns, we can identify more detailed and diverse immune cells, providing insights into understanding the complex immune landscape in cancer ecosystems. Materials and Methods: This study was performed by obtaining publicly available Cellular Indexing of Transcriptomes and Epitopes by Sequencing (CITE-seq) data of peripheral blood mononuclear cells (PBMCs) from the Gene Expression Omnibus database. A total of 94674 total cells were analyzed, of which 32412 were T cells. There were 228 protein features and 16262 mRNA features in the data.The Seurat package was used for quality control and preprocessing, principal component analysis was performed, and Uniform Manifold Approximation and Projection was used to visualize the clusters. Protein and mRNA levels in the CITE-seq were analyzed. Results: We observed that a subset of T cells in the clusters generated at the protein level divided better. By identifying mRNA markers that were highly correlated with the CD4 and CD8 proteins and cross-validating CD26 and CD99 markers using flow cytometry, we found that CD4 + and CD8+ T cells were better discriminated in PBMCs. Weighted Nearest Neighbor clustering results identified a previously unobserved T cell subset. Conclusion In this study, we used CITE-seq data to confirm that protein expression patterns could be used to identify cells more precisely. These findings will improve our understanding of the heterogeneity of immune cells in the future and provide valuable insights into the complexity of the immune response in health and disease.

Purpose: By utilizing both protein and mRNA expression patterns, we can identify more detailed and diverse immune cells, providing insights into understanding the complex immune landscape in cancer ecosystems. Materials and Methods: This study was performed by obtaining publicly available Cellular Indexing of Transcriptomes and Epitopes by Sequencing (CITE-seq) data of peripheral blood mononuclear cells (PBMCs) from the Gene Expression Omnibus database. A total of 94674 total cells were analyzed, of which 32412 were T cells. There were 228 protein features and 16262 mRNA features in the data.The Seurat package was used for quality control and preprocessing, principal component analysis was performed, and Uniform Manifold Approximation and Projection was used to visualize the clusters. Protein and mRNA levels in the CITE-seq were analyzed. Results: We observed that a subset of T cells in the clusters generated at the protein level divided better. By identifying mRNA markers that were highly correlated with the CD4 and CD8 proteins and cross-validating CD26 and CD99 markers using flow cytometry, we found that CD4 + and CD8+ T cells were better discriminated in PBMCs. Weighted Nearest Neighbor clustering results identified a previously unobserved T cell subset. Conclusion In this study, we used CITE-seq data to confirm that protein expression patterns could be used to identify cells more precisely. These findings will improve our understanding of the heterogeneity of immune cells in the future and provide valuable insights into the complexity of the immune response in health and disease.